Skip to content

fix: Updated AWS Athena template #3322

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
kevjumba merged 3 commits into from
Nov 6, 2022
Merged

fix: Updated AWS Athena template #3322

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
3 commits
Select commit Hold shift + click to select a range
d316906
Update the template on how to use AWS Athena
younggyu-oh Oct 30, 2022
b443e09
Remove unnecessary imports
younggyu-oh Oct 30, 2022
b58c536
lint & format
younggyu-oh Oct 30, 2022
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
7 changes: 4 additions & 3 deletions sdk/python/feast/templates/athena/feature_repo/feature_store.yaml
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -6,8 +6,9 @@ online_store:
path: online_store.db
offline_store:
type: athena
region: ap-northeast-2
database: sampledb
region: {AWS region}
database: {The database in the data catalog to be used by Athena}
data_source: AwsDataCatalog
s3_staging_location: s3://sagemaker-yelo-test
s3_staging_location: s3://{S3 bucket to be used by Feast}
workgroup: {Workgroup for Athena}
entity_key_serialization_version: 2
35 changes: 22 additions & 13 deletions sdk/python/feast/templates/athena/feature_repo/test_workflow.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -3,16 +3,28 @@

import pandas as pd

from feast import Entity, Feature, FeatureStore, FeatureView, ValueType
from feast import Entity, FeatureStore, FeatureView, Field
from feast.infra.offline_stores.contrib.athena_offline_store.athena_source import (
AthenaSource,
)
from feast.types import Float64, Int64


def test_end_to_end():

try:
fs = FeatureStore(".")

# Before running this test method
# 1. Upload the driver_stats.parquet file to your S3 bucket.
# (https://github.com/feast-dev/feast-custom-offline-store-demo/tree/main/feature_repo/data)
# 2. Using AWS Glue Crawler, create a table in the data catalog. The generated table can be queried through Athena.
# 3. Specify the S3 bucket name, data source(AwsDataCatalog), database name, Athena's workgroup, etc. in feature_store.yaml

fs = FeatureStore("./feature_repo")

# Partition pruning has a significant impact on Athena's query performance and cost.
# If offline feature dataset is large, it is highly recommended to create partitions using date columns such as ('created','event_timestamp')
# The date_partition_column must be in form of YYYY-MM-DD(string) as in the beginning of the date column.

driver_hourly_stats = AthenaSource(
timestamp_field="event_timestamp",
Expand All @@ -21,31 +33,29 @@ def test_end_to_end():
database="sampledb",
data_source="AwsDataCatalog",
created_timestamp_column="created",
# date_partition_column="std_date"
# date_partition_column="std_date" #YYYY-MM-DD
)

driver = Entity(
name="driver_id",
value_type=ValueType.INT64,
description="driver id",
)

driver_hourly_stats_view = FeatureView(
name="driver_hourly_stats",
entities=["driver_id"],
ttl=timedelta(days=365),
features=[
Feature(name="conv_rate", dtype=ValueType.FLOAT),
Feature(name="acc_rate", dtype=ValueType.FLOAT),
Feature(name="avg_daily_trips", dtype=ValueType.INT64),
entities=[driver],
ttl=timedelta(days=500),
schema=[
Field(name="conv_rate", dtype=Float64),
Field(name="acc_rate", dtype=Float64),
Field(name="avg_daily_trips", dtype=Int64),
],
online=True,
batch_source=driver_hourly_stats,
source=driver_hourly_stats,
)

# apply repository
fs.apply([driver_hourly_stats, driver, driver_hourly_stats_view])

print(fs.list_data_sources())
print(fs.list_feature_views())

Expand All @@ -54,7 +64,6 @@ def test_end_to_end():
)

# Read features from offline store

feature_vector = (
fs.get_historical_features(
features=["driver_hourly_stats:conv_rate"], entity_df=entity_df
Expand Down